Menu

Tag Archives: BBC

Experimental research into using human brainwaves as an alternative form of input to control computers and prosthetic devices has been underway for a number of years. This technology is often referred to as neural interfaces or brain-computer interfaces. The results thus far have generally been promising. Here is a roundup of reports on ExtremeTech.com.

Another early phase neural interface project has been undertaken by the BBC to develop a system enabling a user to mentally select a program from an onscreen television guide. This was reported in a most interesting article entitled The BBC Wants You to Use Your Brain as a Remote Control by Abhimanyu Ghoshal, posted on TheNextWeb.com on June 18, 2015. While still using my keyboard for now, I will sum up, annotate and pose a few questions.

This endeavor, called the Mind Control TV Project, is a joint effort BBC’s digital unit and a user experience (“UX”) firm called This Place. In its current format, the input hardware is a headset that can read human brainwave signals. The article contains three pictures of the hardware and software (which is a customized version of the BBC’s iPlayer app normally used for viewing TV shows on the network).

To choose from among a number of options present onscreen, the user is required to “‘concentrate’ on it” while wearing the headset. That is, to choose a particular option, the user must concentrate upon it “for a few seconds”. A meter in the interface indicates the level of brain activity the user is generating and the “threshold” he or she must reach in order to initiate their choice.

The BBC hopes that this research will, in the future, benefit people with physical and neural disabilities that restrict their movements.

My questions are as follows:

Could this system eventually be so miniaturized that it could be integrated into an ordinary pair of glasses, perhaps Google Glass or something else?

Notwithstanding the significant benefits mentioned in this article, what other types of apps and systems might also find advantages in adapting neural interfaces?

What entrepreneurial opportunities might be waiting out there as a result of this technology?

How might neural interfaces be integrated with the current wave of virtual and augmented reality systems (covered in these seven recent Subway Fold posts), about to very soon enter the consumer market?

The who, what, where and when of the news business has been dramatically transformed by the exponential growth of social media. The instantaneous reporting and viral spread of events happening everywhere across the globe is being enabled by the giants of this industry including, among others, Twitter, Facebook, YouTube and Instagram. Moreover, not only are these platforms acquiring and disseminating news at web-speed, they are often shaping the news and, even in some instances, affecting its outcomes.

Two very compelling and highly related posts have appeared online within the past five days that provide an opportunity to examine this rapidly evolving phenomenon. In both cases, I very highly recommend clicking through and reading both of these in their entirety. I will recap, annotate and add some questions to these posts to start off this topic.

According to this report, the increasing velocity of change occurring in social technology (as well as mobile¹), is deeply affecting the distribution of news and, moreover, how journalists are doing their jobs. To study this in-depth for the US market, Edelman worked with two startups called NewsWhip and Muck Rack. Their study focused on a series of highly shared articles in six topic areas. In turn, their findings were used to create a survey distributed to 250 journalists. Among the survey’s participants the findings were as follows:

More than 75% of those surveyed now took their stories’ social sharing potential into consideration.

The addition of multimedia, reducing copy length, reporting locally, and “more use of human voice and a proximity to trending topics” are being deployed in efforts to raise social sharing.

Almost 75% are adding supplemental videos

They also identified the leading current trends as “more mobile friendly content, faster turnaround times, more original video, smaller newsroom staff and social media growing in influence”.

Edelman will soon publish a more detailed analysis of their data in order to plot the “genome” of a news story being shared across social media. Until then, their “top line analysis” showed that Facebook is the leading social platform for news sharing, with the greatest sources of shares upon it being The Huffington Post (which also ranked highest for video shares), BuzzFeed, Mashable and PlayBuzz; on Twitter, the leading sources come from the BBC, The New York Times and Mashable; and on LinkedIn they are Forbes, The New York Times and Business Insider.

This post then includes a well designed and embedded infographic² clearly delineating the four major trends in Edelman’s study and their conclusions termed “Storytelling Takeaway” for each one. This could be quite helpful to people working in many sectors of news reporting, delivery and strategy.

Social media and news-parsing algorithms have created a whole new universe of “intermediaries between the media and their audiences” who are affecting the production and selection of news. The participants in the news industry must therefore devise and implement strategies that integrate all of these factors to reach their critical goals of being distinguishable and indeed heard at a time of ever-increasing competition for their audience’s attention.

The second post that I found to be helpful in trying to understanding the latest developments here is entitled How Much Work the NYT Has to Do on Social Sharing, in One Chart, by Matthew Ingram on Gigaom.com on January 19, 2015. This is Ingram’s follow up to his earlier post on Gigaom.com on January 14, 2015 entitled News Flash for the NYT: You and BuzzFeed Aren’t That Different in which he argued that both media companies were essentially in the same business, despite their differing demographics and content. Moreover, that BuzzFeed was winning in its efforts to understand and leverage social platforms. Both posts cover a number of challenging issues and referring links to other posts about the very nature, value and processes of the social sharing of news stories and related content.

In regards the writer’s latter post, he ties to strengthen his analysis by presenting two data graphics that compare social news sharing data for both BuzzFeed and NYTimes.com, as well as six other competing news sites. The first is entitled “Mean and average number of shares per article November 2014”. As between the two measurements, the mean for BuzzFeed was 7950 and then 851 for NYTimes.com, while the median for them, respectively, was 966 and 11. Focusing more on the median figures, Ingram concluded that the degree of social sharing of a BuzzFeed article is nearly 100 times greater than that of a NYTimes.com article. (Just to be picky about it, 966/11 = 87.8 or just about 88 times greater.)

Ingram cites and links to a blog post on January 16, 2015 on Medium.com entitled No, BuzzFeed Isn’t “Beating” the New York Times by Simon Owens, rebutting his own conclusions. Owen points to a number of other factors such as content, revenue models and streams, marketing segments, and corporate histories. For him, article sharing data is interesting, but not, in and of itself, positive proof of the two companies being in the same business. Ingram reiterates his opinion that whether visitors are paying for content or not, the understanding social distribution is still important and that the NYTimes.com “needs to step up its game” in this regard.

Ingram concludes with another bar chart entitled “Distribution of articles shared” wherein the same eight news sites are measured in regards to the percentages of their articles that are, according to set numerical criteria, either “Unnoticed”, “Popular”, “Undershared”, “Viral” or “Niche”. With regard to “Unnoticed”, the NYTimes data point is approximately 65% while BuzzFeed is approximately 12%. BuzzFeed also has higher percentages of it articles in the “Viral”, “Popular” and “Niche” categories than the NYTimes.com.

My own questions are as follows:

What refinements, if any, are needed to tweak these analytics in order to better measure and assess the degree and strategic value of a news website’s social sharing?

Does a higher level of social sharing necessarily correlate to higher revenues?

Assuming that a higher level of social sharing exists for Site A, will Site B with a lower level of social sharing still possibly be able to generate more revenue than Site A because its sharing is concentrated among consumers more likely to purchase Site B’s good and services? That is, does each social share for Site B somehow have intrinsically more value that a social share for Site A?

Should market planners, demographers, and content strategists plan their budgets and business campaigns focused only upon increased social sharing numbers, or is this just another variable to be weighted accordingly and then plugged into a spreadsheet in combinations with many other variables? (For any Excel enthusiasts, wouldn’t this make for an interesting application of a pivot table to tabulate and test this data?)